- Generell sind Fragen an dich in den Chunks (glaub auch nur beim Data Load) mit ??? gekennzeichnet, erleichter die Suche ;)

- Sixtrix hat nur sehr wenige Datenpunkte/Keywords (2,368/10,000) -> ist halt so, wird dann vermerkt.

- Data cleaning angepasst - columns nun wie gewünscht

- Dadurch ergeben sich zwei Probleme:
-> Die Lollipos ergeben jetzt leider für Difficulty keinen Sinn mehr, da GKP das nicht ausgibt und somit kein Vergleich möglich ist. Schade. Eine andere baseline? Oder hast du eine andere Idee? -> SECockpit isnd die einzigen mit einem Difficulty Maximum über 100, also anscheinend eine andere range… Hab auf die Schnelle kein maximal möglichen Wert gefunden und daher das max(diff) als 100% gesetzt.

- Bisher unterscheiden sich die Farben der Engine zwischen Ch. 2 und Ch. 3, 4 & 5. Auch generell nicht so simpel, da ja einige bei Ch. 2 zwei Mal auftauchen (SEMrush, Ahrefs). Kann das so zum Kunden oder soll ich das fixen? Im finalen dann sicher, aber evtl. bekommen wir das Problem ja gar nicht…? 

## save plots?
#save <- TRUE
save <- FALSE

## packages
required_packages <- c("tidyverse", "readxl", "ggthemes", "hrbrthemes", "extrafont", "plotly", "scales", "stringr", "gganimate", "here", "tidytext", "sentimentr", "scales", "DT", "here", "sm", "mblm", "prettydoc", "reshape2", "treemapify", "glue", "magick", "imager", "fs", "knitr", "DataExplorer", "inspectdf", "rmdformats", "prettydoc", "janitor", "showtext", "ggbeeswarm", "ggtext", "kableExtra", "rcartocolor", "ggsci", "patchwork")

for(i in required_packages) { 
  if(!require(i, character.only = T)) {
    #  if package is not existing, install then load the package
    install.packages(i, dependencies = T)
    library(i, character.only = T)
  }
}

## theme updates
font_add_google("Montserrat", "Montserrat")

theme_set(ggthemes::theme_clean(base_size = 15, base_family = "Montserrat"))

theme_update(plot.background = element_rect(color = NA),
             #panel.background = element_rect(color = "grey20", size = .4), ## turn off if lighter version
             plot.title = element_markdown(size = 18, hjust = .5, face = "plain"),
             plot.subtitle = element_text(size = 12, hjust = .5, face = "plain"),
             axis.line.x = element_line(color = "grey20"),  ## turn off if full panel border
             axis.line.y = element_line(color = "grey20"),  ## turn off if full panel border
             #axis.line.x = element_blank(), axis.line.y = element_blank(), ## turn off if lighter version
             axis.ticks = element_line(color = "grey20", size = .3),
             axis.title.x = element_text(face = "plain", margin = margin(t = 10)),
             axis.title.y = element_text(face = "plain", margin = margin(r = 10)),
             axis.text = element_text(color = "grey20"),
             legend.title = element_text(color = "grey33"),
             legend.text = element_text(family = "Montserrat", size = 10, color = "grey33"),
             legend.background = element_rect(fill = NA, color = NA))

## theme settings for flipped plots
theme_flip <- 
  theme(panel.grid.major.x = element_line(colour = "gray", linetype = "dotted", size = .25),
        panel.grid.major.y = element_blank(),
        axis.title = element_text(face = "bold"),
        axis.text.y = element_text(face = "bold"),
        legend.position = "bottom")

## theme settings for facet plots
theme_facet <-
  theme(axis.text.y = element_text(face = "plain", size = 10),
        axis.text.x = element_text(size = 8),
        strip.text = element_text(face = "bold"),
        plot.margin = margin(10, 10, 10, 25),
        panel.spacing = unit(1, "lines"))

## numeric format for labels
num_format <- format_format(big.mark = ",", small.mark = ",", scientific = F)

1 Introduction

There is a wealth of Software-as-a-Service (SaaS) companies today that offer a range of keyword analytics data, such as keyword search volume and keyword difficulty scores. For the end user, it is often challenging to make informed comparisons between different data providers, which may be skewed or misleading when viewed individually.

This large-scale analysis of Keyword Analytics data aims to compare data across various SEO tools.More specificially we will do a comparision of the following variables:

  • Keyword Suggestions
  • Average Monthly Search Volume
  • Keyword Difficulty
  • Cost-per-Click Ads data (in US dollar)

We compare the data of a total of 10 tools:

  • Google Keyword Planner
  • Ahrefs
  • SEMrush
  • Moz
  • KeywordTool.io
  • KWfinder
  • LongtailPro
  • SEOCockpit
  • Sixtrix
  • Ubersuggest (only Keyword Suggestions)
  • Majestic (only Keyword Suggestions???)
## path to processed data
rmd_tools <- here("proc_data", "tools.Rmd")

## if data is not available, load, clean and bind datasets
if(!file.exists(rmd_tools)){
  ### load data #################################################################
  
  ## Google Keyword Planner Data (Tool #1)
  df_gkp_raw <- read_csv(here("raw_data", "Step3_Tools_data", "1_Google_keyword_Planer_basis", "GKP_data_final_split.csv"))
  
  ## Ahrefs Data (Tool #2)
  df_ahr_raw <- read_csv(here("raw_data", "Step3_Tools_data", "2_Ahrefs", "export", "ahrefs_export.csv"))
  ## contains less rows than GKP data as the tool does not have enough data points. Should go into the analysis.
  
  ## Moz (Tool #3)
  path <- here("raw_data", "Step3_Tools_data", "3_Moz", "export")
  file_names_csv <- list.files(path = path, recursive = T, full.names = T) 
  file <- map(file_names_csv, 
              function(x) read_csv(x, skip = 13) %>% 
                            mutate(`Min Volume` = as.numeric(`Min Volume`),
                                   `Max Volume` = as.numeric(`Max Volume`))) 
  names(file) <- gsub(".csv","", list.files(path, full.names = F), fixed = T)
  
  df_moz_raw <- 
    bind_rows(file, .id = "column_name") %>% 
    clean_names() 
  
  ## SEMRush (Tool #4)
  path <- here("raw_data", "Step3_Tools_data", "4_SemRush", "export")
  file_names_csv <- list.files(path = path, recursive = T, full.names = T) 
  file <- map(file_names_csv, read_csv) 
  names(file) <- gsub(".csv","", list.files(path, full.names = F), fixed = T)
  
  df_sem_raw <- 
    bind_rows(file, .id = "column_name") %>% 
    clean_names() 
  
  ## KeywordTool.io (Tool #5)
  path <- here("raw_data", "Step3_Tools_data", "5_Keywordtool", "export")
  file_names_csv <- list.files(path = path, recursive = T, full.names = T) 
  file <- map(file_names_csv, read_csv) 
  names(file) <- gsub(".csv","", list.files(path, full.names = F), fixed = T)
  
  df_kwt_raw <- 
    bind_rows(file, .id = "column_name") %>% 
    clean_names() 
  
  ## KWFinder (Tool #6)
  path <- here("raw_data", "Step3_Tools_data", "6_KWfinder", "export")
  file_names_csv <- list.files(path = path, recursive = T, full.names = T) 
  file <- map(file_names_csv, read_csv) 
  names(file) <- gsub(".csv","", list.files(path, full.names = F), fixed = T)
  
  df_kwf_raw <- 
    bind_rows(file, .id = "column_name") %>% 
    clean_names() 
  
  ## LongtailPro (Tool #7)
  path <- here("raw_data", "Step3_Tools_data", "7_LongtailPro", "export")
  file_names_csv <- list.files(path = path, recursive = T, full.names = T) 
  file <- map(file_names_csv, read_csv) 
  names(file) <- gsub(".csv","", list.files(path, full.names = F), fixed = T)
  
  df_ltp_raw <- 
    bind_rows(file, .id = "column_name") %>% 
    clean_names() 
  
  ## SECockpit (Tool #8)
  path <- here("raw_data", "Step3_Tools_data", "8_SECockpit", "export")
  file_names_csv <- list.files(path = path, recursive = T, full.names = T) 
  file <- map(file_names_csv, function(x) read_csv2(x)) 
  names(file) <- gsub(".csv","", list.files(path, full.names = F), fixed = T)
  
  df_sec_raw <- 
    bind_rows(file, .id = "column_name") %>% 
    clean_names() 
  
  ## Sixtrix (Tool #9)
  path <- here("raw_data", "Step3_Tools_data", "9_Sixtrix", "export")
  file_names_csv <- list.files(path = path, recursive = T, full.names = T) 
  file <- map(file_names_csv, read_csv2) 
  names(file) <- gsub(".csv","", list.files(path, full.names = F), fixed = T)
  
  df_six_raw <- 
    bind_rows(file, .id = "column_name") %>% 
    clean_names() 
  
  
  ### clean data ################################################################
  
  ## select relevant columns and bind all tools
  ## Google Key Planner
  df_gkp <-
    df_gkp_raw %>% 
    mutate(
      tool_id = 1,
      tool = "Google Keyword Planner"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword,
      volume = "avg_monthly_searches.x",
      cpc_min = "top_of_page_bid_low_range.y",
      cpc_max = "top_of_page_bid_high_range.y"
    ) %>% 
    mutate(
      diff = NA_real_,  ## GKP dies not have a difficulty score output
      cpc = cpc_max - (cpc_max - cpc_min) / 2     
    ) %>% 
    dplyr::select(-cpc_min, cpc_max)
  
  ## Ahrefs
  df_ahr <-
    df_ahr_raw %>% 
    mutate(
      tool_id = 2,
      tool = "Ahrefs"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword = "Keyword",
      volume = "Volume",
      diff = "Difficulty", 
      cpc = "CPC"
    ) 
  
  ## Moz
  df_moz <-
    df_moz_raw %>% 
    mutate(
      tool_id = 3,
      tool = "Moz (mix of sources)"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword,
      max_volume, 
      min_volume,
      diff = "difficulty"
    ) %>% 
    mutate(
      cpc = NA_real_,  ## Moz has no CPC output
      volume = round(max_volume - (max_volume - min_volume) / 2, 0)
    ) %>% 
    dplyr::select(-max_volume, -min_volume)
  
  ## SEMrush
  df_sem <-
    df_sem_raw %>% 
    mutate(
      tool_id = 4,
      tool = "SEMrush"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword,
      volume,
      diff = "keyword_difficulty", 
      cpc = "cpc_usd"
    ) 
  
  ## KeywordTool.io
  df_kwt <-
    df_kwt_raw %>% 
    mutate(
      tool_id = 5,
      tool = "KeywordTool.io"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword = "keywords",
      volume = "search_volume_average",
      diff = "competition", 
      cpc = "cpc_usd"
    ) 
  
  ## KWFinder
  df_kwf <-
    df_kwf_raw %>% 
    mutate(
      tool_id = 6,
      tool = "KWFinder"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword,
      volume = "avg_search_volume",
      diff = "keyword_difficulty", 
      cpc
    ) 
  
  df_ltp <-
    df_ltp_raw %>% 
    mutate(
      tool_id = 7,
      tool = "LongtailPro"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword = "keywords",
      volume,
      diff = "avg_kc", 
      cpc = "bid"
    ) %>% 
    mutate(cpc = as.numeric(str_sub(cpc, 2)))
  
  ## SECockpit
  df_sec <-
    df_sec_raw %>% 
    mutate(
      tool_id = 8,
      tool = "SECockpit"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword = "phrase",
      volume = "monthly_searches",
      diff = "top_results", 
      cpc
    ) %>% 
    mutate(
      cpc = as.numeric(cpc),
      diff = diff / max(diff) * 100  ## ??? top_results has a range from 6 to 464. Not sure hwat the possible max is so I take 464 for now
    )  
  
  ## Sixtrix
  df_six <-
    df_six_raw %>% 
    mutate(
      tool_id = 9,
      tool = "Sixtrix"
    ) %>% 
    dplyr::select(
      tool_id, tool,
      keyword = "x_u_feff_keyword",
      volume = "search_volume",
      diff = "competition",
      cpc
    ) %>% 
    mutate(
      ## extract max volume from range string
      volume = str_replace(volume, ".*\\s", ""),  ## using the max of the range here
      volume = str_replace(volume, "\\.", ""),
      thousands = if_else(str_detect(volume, ".*K"), T, F),  ## dealing with "K" units
      volume = str_replace(volume, "K", ""),
      volume = if_else(thousands == T, 
                       as.numeric(volume) * 1000, 
                       as.numeric(volume)),
      ## turn difficulty into numbers
      diff = as.numeric(diff), ## turns "-" to NA - guess that's good!
      ## clean cpc and convert to USD
      cpc = str_replace(cpc, ",", "."),
      cpc = str_sub(cpc, 1, -4),
      cpc = as.numeric(cpc) * 1.11 ## EUR -> USD, exchange rate 2020-01-16 05:46 pm
    ) %>% 
    dplyr::select(-thousands)
  
  
  ### final dataset #############################################################
  
  ## join all tools
  df_tools <-
    df_gkp %>% 
    bind_rows(df_ahr) %>% 
    bind_rows(df_moz) %>% 
    bind_rows(df_sem) %>% 
    bind_rows(df_kwt) %>% 
    bind_rows(df_kwf) %>% 
    bind_rows(df_ltp) %>% 
    bind_rows(df_sec) %>% 
    bind_rows(df_six) %>% 
    mutate(
      tool = factor(tool, levels = unique(tool)),
      volume_cat = case_when(
        volume <= 100 ~ "0-100",
        volume > 100 & volume <= 1000 ~ "100-1000",
        volume > 1000 & volume <= 10000 ~ "1000-10000",
        volume > 100000 ~ "10000+",
      )
    ) %>% 
    filter(cpc > 0) ## remove $0 "costs"
  
  saveRDS(df_tools, file = rmd_tools)

}else{
  df_tools <- readRDS(rmd_tools)
}

## long format with volume, difficulty + cpc as one column 'cateogry'
df_tools_long <-
  df_tools %>% 
  dplyr::select(-tool_id, -volume_cat) %>% 
  pivot_longer(c(-tool, -keyword),
               names_to = "category",
               values_to = "value")

2 Keyword Suggestions

2.1 Data Overview

## path to processed data
rmd_kws <- here("proc_data", "keyword_suggestions.Rmd")

## load version 1 csv
df_kw_raw <- read_csv(here("raw_data", "Step3_Tools_data", "keyword_suggestions", "keyword_suggestions_v1.csv")) %>% 
  clean_names() 

if(!file.exists(rmd_kws)){
  
  ### clean data ################################################################
  
  ## Transform data into long format (two columns with *SEO tool* (`tool`) 
  ## and *suggested keywords* (`suggested`))
  df_kw <-
    df_kw_raw %>% 
    mutate_at(vars(se_mrush_broad_match:majestic), as.numeric) %>% 
    pivot_longer(
      cols = google_keyword_planner:majestic,
      names_to = "tool",
      values_to = "suggested"
    ) %>% 
    ## remove not avilable values
    filter(!is.na(suggested)) %>%
    ## remove combination of keyword search for now (since not all provide it)
    filter(!str_detect(keyword, "All 5 KW")) %>% 
    ## Clean SEO tool names
    mutate(tool = 
      factor(
        tool,
        levels = c("google_keyword_planner", 
                   "ahrefs_all_keyword_ideas", 
                   "ahrefs_phrase_match", 
                   "se_mrush_broad_match", 
                   "se_mrush_phrase_match", 
                   "moz_include_a_mix_of_sources", 
                   "ubersuggest_related", 
                   "se_cockpit", 
                   "keyword_tool_io", 
                   "kw_finder"),
        labels = c("Google Keyword Planner", 
                   "Ahrefs (all keyword ideas)", 
                   "Ahrefs (phrase match)", 
                   "SEMrush (broad match)", 
                   "SEMrush (phrase match)", 
                   "Moz (mix of sources)", 
                   "Ubersuggest (related)", 
                   "SECockpit", 
                   "KeywordTool.io", 
                   "KWFinder")
        )
    ) %>% 
    ## Sort SEO tools and keyword categories by mean number of suggestions
    group_by(tool) %>% 
    mutate(tool_median = median(suggested)) %>% 
    group_by(category) %>% 
    mutate(category_median = median(suggested)) %>% 
    ungroup() %>% 
    mutate(
      tool_fct = fct_reorder(tool, tool_median),
      category_fct = fct_reorder(category, category_median),
    )
  
  ### final dataset ########################################################
  
  
  saveRDS(df_kw, file = rmd_kws)
  
}else{
  df_kw <- readRDS(rmd_kws)
}

## range of suggestions
range_kw <- range(df_kw$suggested)

## number of keywords per cateory 
## (based on the un-cleanded data since we have removed NAs)
n_kw_cat <-
  df_kw_raw %>% 
  group_by(category) %>% 
  count() %>% 
  ungroup() %>% 
  summarize(n = unique(n)) %>% 
  pull(n)

There are 7 SEO tools, each used to search 75 keywords. The keywords fall into 15 categories with each category containing 6. Minimum of suggestions was 22 and the highest values was 6.58880510^{6}.

Category Keyword SEO Tool Suggestions Median per Tool Median per Category
Financial Services finance Google Keyword Planner 691 691 153,308.0
Ahrefs (all keyword ideas) 1,269,225 43,488 153,308.0
Ahrefs (phrase match) 1,233,201 23,021 153,308.0
SEMrush (broad match) 1,201,273 36,523 153,308.0
SEMrush (phrase match) 973,555 25,475 153,308.0
Moz (mix of sources) 1,000 1,000 153,308.0
Ubersuggest (related) 196,562 4,261 153,308.0
investing Google Keyword Planner 2,528 691 153,308.0
Ahrefs (all keyword ideas) 264,668 43,488 153,308.0
Ahrefs (phrase match) 255,608 23,021 153,308.0
SEMrush (broad match) 1,209,214 36,523 153,308.0
SEMrush (phrase match) 135,075 25,475 153,308.0
Moz (mix of sources) 1,000 1,000 153,308.0
Ubersuggest (related) 29,295 4,261 153,308.0
loans Google Keyword Planner 936 691 153,308.0
Ahrefs (all keyword ideas) 1,007,861 43,488 153,308.0
Ahrefs (phrase match) 994,674 23,021 153,308.0
SEMrush (broad match) 1,851,628 36,523 153,308.0
SEMrush (phrase match) 541,163 25,475 153,308.0
Moz (mix of sources) 1,000 1,000 153,308.0
Ubersuggest (related) 153,308 4,261 153,308.0
insurance Google Keyword Planner 789 691 153,308.0
Ahrefs (all keyword ideas) 6,588,805 43,488 153,308.0
Ahrefs (phrase match) 6,522,620 23,021 153,308.0
SEMrush (broad match) 4,109,928 36,523 153,308.0
SEMrush (phrase match) 4,003,105 25,475 153,308.0
Moz (mix of sources) 1,000 1,000 153,308.0
Ubersuggest (related) 823,235 4,261 153,308.0
financial advisor Google Keyword Planner 695 691 153,308.0
Ahrefs (all keyword ideas) 74,293 43,488 153,308.0
Ahrefs (phrase match) 68,696 23,021 153,308.0
SEMrush (broad match) 59,695 36,523 153,308.0
SEMrush (phrase match) 41,971 25,475 153,308.0
Moz (mix of sources) 1,000 1,000 153,308.0
Ubersuggest (related) 9,373 4,261 153,308.0
Diet how to lose weight Google Keyword Planner 1,648 691 10,055.0
Ahrefs (all keyword ideas) 92,045 43,488 10,055.0
Ahrefs (phrase match) 30,342 23,021 10,055.0
SEMrush (broad match) 45,852 36,523 10,055.0
SEMrush (phrase match) 43,474 25,475 10,055.0
Moz (mix of sources) 1,000 1,000 10,055.0
Ubersuggest (related) 10,055 4,261 10,055.0
low carb Google Keyword Planner 1,378 691 10,055.0
Ahrefs (all keyword ideas) 319,292 43,488 10,055.0
Ahrefs (phrase match) 296,019 23,021 10,055.0
SEMrush (broad match) 188,911 36,523 10,055.0
SEMrush (phrase match) 180,554 25,475 10,055.0
Moz (mix of sources) 1,000 1,000 10,055.0
Ubersuggest (related) 40,634 4,261 10,055.0
nutrition Google Keyword Planner 1,025 691 10,055.0
Ahrefs (all keyword ideas) 1,490,264 43,488 10,055.0
Ahrefs (phrase match) 1,481,436 23,021 10,055.0
SEMrush (broad match) 1,317,219 36,523 10,055.0
SEMrush (phrase match) 1,112,682 25,475 10,055.0
Moz (mix of sources) 1,000 1,000 10,055.0
Ubersuggest (related) 223,859 4,261 10,055.0
diet plans Google Keyword Planner 1,766 691 10,055.0
Ahrefs (all keyword ideas) 29,129 43,488 10,055.0
Ahrefs (phrase match) 6,850 23,021 10,055.0
SEMrush (broad match) 85,786 36,523 10,055.0
SEMrush (phrase match) 4,025 25,475 10,055.0
Moz (mix of sources) 1,000 1,000 10,055.0
Ubersuggest (related) 1,693 4,261 10,055.0
weight loss tips Google Keyword Planner 907 691 10,055.0
Ahrefs (all keyword ideas) 17,659 43,488 10,055.0
Ahrefs (phrase match) 2,828 23,021 10,055.0
SEMrush (broad match) 3,865 36,523 10,055.0
SEMrush (phrase match) 3,756 25,475 10,055.0
Moz (mix of sources) 1,000 1,000 10,055.0
Ubersuggest (related) 1,787 4,261 10,055.0
Online Marketing marketing Google Keyword Planner 284 691 34,153.0
Ahrefs (all keyword ideas) 2,285,404 43,488 34,153.0
Ahrefs (phrase match) 2,281,425 23,021 34,153.0
SEMrush (broad match) 5,068,178 36,523 34,153.0
SEMrush (phrase match) 1,544,835 25,475 34,153.0
Moz (mix of sources) 1,000 1,000 34,153.0
Ubersuggest (related) 378,278 4,261 34,153.0
seo Google Keyword Planner 428 691 34,153.0
Ahrefs (all keyword ideas) 362,649 43,488 34,153.0
Ahrefs (phrase match) 351,981 23,021 34,153.0
SEMrush (broad match) 234,768 36,523 34,153.0
SEMrush (phrase match) 229,065 25,475 34,153.0
Moz (mix of sources) 1,000 1,000 34,153.0
Ubersuggest (related) 77,664 4,261 34,153.0
digital marketing Google Keyword Planner 377 691 34,153.0
Ahrefs (all keyword ideas) 127,183 43,488 34,153.0
Ahrefs (phrase match) 106,089 23,021 34,153.0
SEMrush (broad match) 102,613 36,523 34,153.0
SEMrush (phrase match) 96,133 25,475 34,153.0
Moz (mix of sources) 1,000 1,000 34,153.0
Ubersuggest (related) 30,000 4,261 34,153.0
affiliate marketing Google Keyword Planner 1,206 691 34,153.0
Ahrefs (all keyword ideas) 31,233 43,488 34,153.0
Ahrefs (phrase match) 25,350 23,021 34,153.0
SEMrush (broad match) 18,828 36,523 34,153.0
SEMrush (phrase match) 17,065 25,475 34,153.0
Moz (mix of sources) 1,000 1,000 34,153.0
Ubersuggest (related) 6,128 4,261 34,153.0
marketing plan Google Keyword Planner 543 691 34,153.0
Ahrefs (all keyword ideas) 54,207 43,488 34,153.0
Ahrefs (phrase match) 41,784 23,021 34,153.0
SEMrush (broad match) 44,816 36,523 34,153.0
SEMrush (phrase match) 34,153 25,475 34,153.0
Moz (mix of sources) 1,000 1,000 34,153.0
Ubersuggest (related) 8,840 4,261 34,153.0
Women Fashion dresses Google Keyword Planner 1,545 691 39,866.0
Ahrefs (all keyword ideas) 1,337,414 43,488 39,866.0
Ahrefs (phrase match) 1,240,915 23,021 39,866.0
SEMrush (broad match) 4,447,697 36,523 39,866.0
SEMrush (phrase match) 767,753 25,475 39,866.0
Moz (mix of sources) 1,000 1,000 39,866.0
Ubersuggest (related) 293,656 4,261 39,866.0
clothes Google Keyword Planner 2,336 691 39,866.0
Ahrefs (all keyword ideas) 2,019,714 43,488 39,866.0
Ahrefs (phrase match) 1,991,817 23,021 39,866.0
SEMrush (broad match) 2,886,463 36,523 39,866.0
SEMrush (phrase match) 1,076,138 25,475 39,866.0
Moz (mix of sources) 1,000 1,000 39,866.0
Ubersuggest (related) 272,112 4,261 39,866.0
fashion Google Keyword Planner 1,290 691 39,866.0
Ahrefs (all keyword ideas) 1,856,785 43,488 39,866.0
Ahrefs (phrase match) 1,805,903 23,021 39,866.0
SEMrush (broad match) 1,386,122 36,523 39,866.0
SEMrush (phrase match) 1,224,626 25,475 39,866.0
Moz (mix of sources) 1,000 1,000 39,866.0
Ubersuggest (related) 310,343 4,261 39,866.0
cocktail dresses Google Keyword Planner 1,600 691 39,866.0
Ahrefs (all keyword ideas) 39,866 43,488 39,866.0
Ahrefs (phrase match) 14,936 23,021 39,866.0
SEMrush (broad match) 34,840 36,523 39,866.0
SEMrush (phrase match) 13,029 25,475 39,866.0
Moz (mix of sources) 1,000 1,000 39,866.0
Ubersuggest (related) 4,216 4,261 39,866.0
plus size dresses Google Keyword Planner 1,884 691 39,866.0
Ahrefs (all keyword ideas) 100,985 43,488 39,866.0
Ahrefs (phrase match) 9,935 23,021 39,866.0
SEMrush (broad match) 60,499 36,523 39,866.0
SEMrush (phrase match) 27,851 25,475 39,866.0
Moz (mix of sources) 1,000 1,000 39,866.0
Ubersuggest (related) 2,520 4,261 39,866.0
Home Improvement construction Google Keyword Planner 369 691 20,392.0
Ahrefs (all keyword ideas) 2,474,705 43,488 20,392.0
Ahrefs (phrase match) 2,471,944 23,021 20,392.0
SEMrush (broad match) 1,964,290 36,523 20,392.0
SEMrush (phrase match) 1,782,879 25,475 20,392.0
Moz (mix of sources) 1,000 1,000 20,392.0
Ubersuggest (related) 292,258 4,261 20,392.0
renovation Google Keyword Planner 369 691 20,392.0
Ahrefs (all keyword ideas) 136,107 43,488 20,392.0
Ahrefs (phrase match) 133,467 23,021 20,392.0
SEMrush (broad match) 166,948 36,523 20,392.0
SEMrush (phrase match) 96,176 25,475 20,392.0
Moz (mix of sources) 1,000 1,000 20,392.0
Ubersuggest (related) 21,632 4,261 20,392.0
bathroom remodel Google Keyword Planner 677 691 20,392.0
Ahrefs (all keyword ideas) 25,836 43,488 20,392.0
Ahrefs (phrase match) 15,058 23,021 20,392.0
SEMrush (broad match) 20,392 36,523 20,392.0
SEMrush (phrase match) 12,416 25,475 20,392.0
Moz (mix of sources) 1,000 1,000 20,392.0
Ubersuggest (related) 3,241 4,261 20,392.0
kitchen remodel Google Keyword Planner 653 691 20,392.0
Ahrefs (all keyword ideas) 30,068 43,488 20,392.0
Ahrefs (phrase match) 15,710 23,021 20,392.0
SEMrush (broad match) 21,215 36,523 20,392.0
SEMrush (phrase match) 12,296 25,475 20,392.0
Moz (mix of sources) 1,000 1,000 20,392.0
Ubersuggest (related) 3,358 4,261 20,392.0
general contractor Google Keyword Planner 451 691 20,392.0
Ahrefs (all keyword ideas) 50,817 43,488 20,392.0
Ahrefs (phrase match) 47,254 23,021 20,392.0
SEMrush (broad match) 42,249 36,523 20,392.0
SEMrush (phrase match) 23,039 25,475 20,392.0
Moz (mix of sources) 1,000 1,000 20,392.0
Ubersuggest (related) 4,261 4,261 20,392.0
Gardening landscaping Google Keyword Planner 464 691 8,082.0
Ahrefs (all keyword ideas) 406,570 43,488 8,082.0
Ahrefs (phrase match) 403,114 23,021 8,082.0
SEMrush (broad match) 767,343 36,523 8,082.0
SEMrush (phrase match) 263,772 25,475 8,082.0
Moz (mix of sources) 1,000 1,000 8,082.0
Ubersuggest (related) 60,255 4,261 8,082.0
landscape design Google Keyword Planner 621 691 8,082.0
Ahrefs (all keyword ideas) 42,455 43,488 8,082.0
Ahrefs (phrase match) 28,504 23,021 8,082.0
SEMrush (broad match) 35,538 36,523 8,082.0
SEMrush (phrase match) 26,587 25,475 8,082.0
Moz (mix of sources) 1,000 1,000 8,082.0
Ubersuggest (related) 6,295 4,261 8,082.0
garden centre Google Keyword Planner 182 691 8,082.0
Ahrefs (all keyword ideas) 37,782 43,488 8,082.0
Ahrefs (phrase match) 8,001 23,021 8,082.0
SEMrush (broad match) 53,150 36,523 8,082.0
SEMrush (phrase match) 47,092 25,475 8,082.0
Moz (mix of sources) 1,000 1,000 8,082.0
Ubersuggest (related) 8,082 4,261 8,082.0
gardening tools Google Keyword Planner 1,622 691 8,082.0
Ahrefs (all keyword ideas) 6,037 43,488 8,082.0
Ahrefs (phrase match) 3,267 23,021 8,082.0
SEMrush (broad match) 16,215 36,523 8,082.0
SEMrush (phrase match) 2,016 25,475 8,082.0
Moz (mix of sources) 1,000 1,000 8,082.0
Ubersuggest (related) 900 4,261 8,082.0
plant nursery Google Keyword Planner 1,342 691 8,082.0
Ahrefs (all keyword ideas) 33,764 43,488 8,082.0
Ahrefs (phrase match) 18,008 23,021 8,082.0
SEMrush (broad match) 26,947 36,523 8,082.0
SEMrush (phrase match) 18,566 25,475 8,082.0
Moz (mix of sources) 1,000 1,000 8,082.0
Ubersuggest (related) 3,720 4,261 8,082.0
Vacations Travel travel agency Google Keyword Planner 554 691 16,370.0
Ahrefs (all keyword ideas) 86,272 43,488 16,370.0
Ahrefs (phrase match) 64,928 23,021 16,370.0
SEMrush (broad match) 70,617 36,523 16,370.0
SEMrush (phrase match) 61,296 25,475 16,370.0
Moz (mix of sources) 1,000 1,000 16,370.0
Ubersuggest (related) 19,655 4,261 16,370.0
all inclusive resorts Google Keyword Planner 706 691 16,370.0
Ahrefs (all keyword ideas) 82,001 43,488 16,370.0
Ahrefs (phrase match) 39,679 23,021 16,370.0
SEMrush (broad match) 48,465 36,523 16,370.0
SEMrush (phrase match) 31,456 25,475 16,370.0
Moz (mix of sources) 1,000 1,000 16,370.0
Ubersuggest (related) 11,771 4,261 16,370.0
vacation packages Google Keyword Planner 709 691 16,370.0
Ahrefs (all keyword ideas) 106,259 43,488 16,370.0
Ahrefs (phrase match) 52,750 23,021 16,370.0
SEMrush (broad match) 45,007 36,523 16,370.0
SEMrush (phrase match) 36,567 25,475 16,370.0
Moz (mix of sources) 1,000 1,000 16,370.0
Ubersuggest (related) 19,248 4,261 16,370.0
cheap vacations Google Keyword Planner 1,145 691 16,370.0
Ahrefs (all keyword ideas) 44,657 43,488 16,370.0
Ahrefs (phrase match) 4,524 23,021 16,370.0
SEMrush (broad match) 17,757 36,523 16,370.0
SEMrush (phrase match) 7,692 25,475 16,370.0
Moz (mix of sources) 1,000 1,000 16,370.0
Ubersuggest (related) 1,254 4,261 16,370.0
travel deals Google Keyword Planner 1,088 691 16,370.0
Ahrefs (all keyword ideas) 49,181 43,488 16,370.0
Ahrefs (phrase match) 15,414 23,021 16,370.0
SEMrush (broad match) 16,370 36,523 16,370.0
SEMrush (phrase match) 14,497 25,475 16,370.0
Moz (mix of sources) 1,000 1,000 16,370.0
Ubersuggest (related) 5,631 4,261 16,370.0
Legal legal aid Google Keyword Planner 326 691 10,094.0
Ahrefs (all keyword ideas) 73,499 43,488 10,094.0
Ahrefs (phrase match) 55,473 23,021 10,094.0
SEMrush (broad match) 45,987 36,523 10,094.0
SEMrush (phrase match) 44,703 25,475 10,094.0
Moz (mix of sources) 1,000 1,000 10,094.0
Ubersuggest (related) 10,114 4,261 10,094.0
law firm Google Keyword Planner 342 691 10,094.0
Ahrefs (all keyword ideas) 294,866 43,488 10,094.0
Ahrefs (phrase match) 289,472 23,021 10,094.0
SEMrush (broad match) 276,454 36,523 10,094.0
SEMrush (phrase match) 224,078 25,475 10,094.0
Moz (mix of sources) 1,000 1,000 10,094.0
Ubersuggest (related) 35,696 4,261 10,094.0
legal separation Google Keyword Planner 99 691 10,094.0
Ahrefs (all keyword ideas) 14,505 43,488 10,094.0
Ahrefs (phrase match) 11,141 23,021 10,094.0
SEMrush (broad match) 10,094 36,523 10,094.0
SEMrush (phrase match) 7,075 25,475 10,094.0
Moz (mix of sources) 1,000 1,000 10,094.0
Ubersuggest (related) 1,532 4,261 10,094.0
attorney at law Google Keyword Planner 788 691 10,094.0
Ahrefs (all keyword ideas) 38,683 43,488 10,094.0
Ahrefs (phrase match) 35,483 23,021 10,094.0
SEMrush (broad match) 23,509 36,523 10,094.0
SEMrush (phrase match) 19,083 25,475 10,094.0
Moz (mix of sources) 1,000 1,000 10,094.0
Ubersuggest (related) 1,311 4,261 10,094.0
free legal advice Google Keyword Planner 589 691 10,094.0
Ahrefs (all keyword ideas) 15,837 43,488 10,094.0
Ahrefs (phrase match) 6,001 23,021 10,094.0
SEMrush (broad match) 5,964 36,523 10,094.0
SEMrush (phrase match) 5,953 25,475 10,094.0
Moz (mix of sources) 1,000 1,000 10,094.0
Ubersuggest (related) 1,397 4,261 10,094.0
Office Supplies office equipment Google Keyword Planner 680 691 1,434.0
Ahrefs (all keyword ideas) 17,639 43,488 1,434.0
Ahrefs (phrase match) 9,452 23,021 1,434.0
SEMrush (broad match) 8,414 36,523 1,434.0
SEMrush (phrase match) 7,679 25,475 1,434.0
Moz (mix of sources) 1,000 1,000 1,434.0
Ubersuggest (related) 1,527 4,261 1,434.0
stationery online Google Keyword Planner 157 691 1,434.0
Ahrefs (all keyword ideas) 33,696 43,488 1,434.0
Ahrefs (phrase match) 717 23,021 1,434.0
SEMrush (broad match) 1,434 36,523 1,434.0
SEMrush (phrase match) 1,426 25,475 1,434.0
Moz (mix of sources) 1,000 1,000 1,434.0
Ubersuggest (related) 432 4,261 1,434.0
cute office supplies Google Keyword Planner 55 691 1,434.0
Ahrefs (all keyword ideas) 2,946 43,488 1,434.0
Ahrefs (phrase match) 145 23,021 1,434.0
SEMrush (broad match) 158 36,523 1,434.0
SEMrush (phrase match) 146 25,475 1,434.0
Moz (mix of sources) 1,000 1,000 1,434.0
Ubersuggest (related) 24 4,261 1,434.0
office furniture Google Keyword Planner 1,940 691 1,434.0
Ahrefs (all keyword ideas) 87,178 43,488 1,434.0
Ahrefs (phrase match) 54,994 23,021 1,434.0
SEMrush (broad match) 59,647 36,523 1,434.0
SEMrush (phrase match) 59,224 25,475 1,434.0
Moz (mix of sources) 1,000 1,000 1,434.0
Ubersuggest (related) 18,078 4,261 1,434.0
office supply store Google Keyword Planner 410 691 1,434.0
Ahrefs (all keyword ideas) 10,032 43,488 1,434.0
Ahrefs (phrase match) 3,372 23,021 1,434.0
SEMrush (broad match) 4,107 36,523 1,434.0
SEMrush (phrase match) 1,974 25,475 1,434.0
Moz (mix of sources) 1,000 1,000 1,434.0
Ubersuggest (related) 250 4,261 1,434.0
Web Hosting free web hosting Google Keyword Planner 1,401 691 2,053.0
Ahrefs (all keyword ideas) 14,315 43,488 2,053.0
Ahrefs (phrase match) 3,559 23,021 2,053.0
SEMrush (broad match) 4,315 36,523 2,053.0
SEMrush (phrase match) 3,972 25,475 2,053.0
Moz (mix of sources) 1,000 1,000 2,053.0
Ubersuggest (related) 1,181 4,261 2,053.0
dedicated server Google Keyword Planner 665 691 2,053.0
Ahrefs (all keyword ideas) 60,702 43,488 2,053.0
Ahrefs (phrase match) 50,602 23,021 2,053.0
SEMrush (broad match) 28,980 36,523 2,053.0
SEMrush (phrase match) 25,485 25,475 2,053.0
Moz (mix of sources) 1,000 1,000 2,053.0
Ubersuggest (related) 3,763 4,261 2,053.0
best web hosting Google Keyword Planner 1,246 691 2,053.0
Ahrefs (all keyword ideas) 11,931 43,488 2,053.0
Ahrefs (phrase match) 2,368 23,021 2,053.0
SEMrush (broad match) 3,358 36,523 2,053.0
SEMrush (phrase match) 2,902 25,475 2,053.0
Moz (mix of sources) 1,000 1,000 2,053.0
Ubersuggest (related) 919 4,261 2,053.0
domain name registration Google Keyword Planner 835 691 2,053.0
Ahrefs (all keyword ideas) 33,962 43,488 2,053.0
Ahrefs (phrase match) 2,053 23,021 2,053.0
SEMrush (broad match) 2,103 36,523 2,053.0
SEMrush (phrase match) 1,943 25,475 2,053.0
Moz (mix of sources) 1,000 1,000 2,053.0
Ubersuggest (related) 718 4,261 2,053.0
cheap domains Google Keyword Planner 217 691 2,053.0
Ahrefs (all keyword ideas) 10,757 43,488 2,053.0
Ahrefs (phrase match) 299 23,021 2,053.0
SEMrush (broad match) 2,288 36,523 2,053.0
SEMrush (phrase match) 429 25,475 2,053.0
Moz (mix of sources) 1,000 1,000 2,053.0
Ubersuggest (related) 81 4,261 2,053.0
Dating free dating sites Google Keyword Planner 757 691 3,951.0
Ahrefs (all keyword ideas) 49,099 43,488 3,951.0
Ahrefs (phrase match) 6,838 23,021 3,951.0
SEMrush (broad match) 24,250 36,523 3,951.0
SEMrush (phrase match) 15,450 25,475 3,951.0
Moz (mix of sources) 1,000 1,000 3,951.0
Ubersuggest (related) 1,946 4,261 3,951.0
women seeking men Google Keyword Planner 243 691 3,951.0
Ahrefs (all keyword ideas) 13,098 43,488 3,951.0
Ahrefs (phrase match) 7,788 23,021 3,951.0
SEMrush (broad match) 3,844 36,523 3,951.0
SEMrush (phrase match) 3,527 25,475 3,951.0
Moz (mix of sources) 1,000 1,000 3,951.0
Ubersuggest (related) 842 4,261 3,951.0
speed dating Google Keyword Planner 267 691 3,951.0
Ahrefs (all keyword ideas) 17,715 43,488 3,951.0
Ahrefs (phrase match) 16,773 23,021 3,951.0
SEMrush (broad match) 31,566 36,523 3,951.0
SEMrush (phrase match) 29,304 25,475 3,951.0
Moz (mix of sources) 1,000 1,000 3,951.0
Ubersuggest (related) 3,865 4,261 3,951.0
interracial dating Google Keyword Planner 194 691 3,951.0
Ahrefs (all keyword ideas) 8,300 43,488 3,951.0
Ahrefs (phrase match) 5,120 23,021 3,951.0
SEMrush (broad match) 3,951 36,523 3,951.0
SEMrush (phrase match) 3,751 25,475 3,951.0
Moz (mix of sources) 1,000 1,000 3,951.0
Ubersuggest (related) 920 4,261 3,951.0
matrimonial Google Keyword Planner 533 691 3,951.0
Ahrefs (all keyword ideas) 24,420 43,488 3,951.0
Ahrefs (phrase match) 23,021 23,021 3,951.0
SEMrush (broad match) 47,687 36,523 3,951.0
SEMrush (phrase match) 15,633 25,475 3,951.0
Moz (mix of sources) 1,000 1,000 3,951.0
Ubersuggest (related) 5,913 4,261 3,951.0
Wedding wedding cakes Google Keyword Planner 928 691 25,253.5
Ahrefs (all keyword ideas) 35,360 43,488 25,253.5
Ahrefs (phrase match) 31,459 23,021 25,253.5
SEMrush (broad match) 94,548 36,523 25,253.5
SEMrush (phrase match) 25,475 25,475 25,253.5
Ubersuggest (related) 7,030 4,261 25,253.5
wedding invitations Google Keyword Planner 716 691 25,253.5
Ahrefs (all keyword ideas) 71,946 43,488 25,253.5
Ahrefs (phrase match) 55,974 23,021 25,253.5
SEMrush (broad match) 90,872 36,523 25,253.5
SEMrush (phrase match) 41,568 25,475 25,253.5
Moz (mix of sources) 1,000 1,000 25,253.5
Ubersuggest (related) 14,621 4,261 25,253.5
wedding planner Google Keyword Planner 333 691 25,253.5
Ahrefs (all keyword ideas) 42,023 43,488 25,253.5
Ahrefs (phrase match) 34,420 23,021 25,253.5
SEMrush (broad match) 36,523 36,523 25,253.5
SEMrush (phrase match) 28,913 25,475 25,253.5
Moz (mix of sources) 1,000 1,000 25,253.5
Ubersuggest (related) 8,859 4,261 25,253.5
bridal dresses Google Keyword Planner 1,390 691 25,253.5
Ahrefs (all keyword ideas) 39,276 43,488 25,253.5
Ahrefs (phrase match) 7,634 23,021 25,253.5
SEMrush (broad match) 25,032 36,523 25,253.5
SEMrush (phrase match) 12,729 25,475 25,253.5
Moz (mix of sources) 1,000 1,000 25,253.5
Ubersuggest (related) 2,490 4,261 25,253.5
veil Google Keyword Planner 339 691 25,253.5
Ahrefs (all keyword ideas) 166,008 43,488 25,253.5
Ahrefs (phrase match) 163,971 23,021 25,253.5
SEMrush (broad match) 119,340 36,523 25,253.5
SEMrush (phrase match) 96,817 25,475 25,253.5
Moz (mix of sources) 1,000 1,000 25,253.5
Ubersuggest (related) 14,198 4,261 25,253.5
Automotive automatic cars Google Keyword Planner 600 691 5,811.0
Ahrefs (all keyword ideas) 11,634 43,488 5,811.0
Ahrefs (phrase match) 3,726 23,021 5,811.0
SEMrush (broad match) 27,619 36,523 5,811.0
SEMrush (phrase match) 9,603 25,475 5,811.0
Moz (mix of sources) 1,000 1,000 5,811.0
Ubersuggest (related) 1,960 4,261 5,811.0
motor car Google Keyword Planner 717 691 5,811.0
Ahrefs (all keyword ideas) 43,488 43,488 5,811.0
Ahrefs (phrase match) 7,871 23,021 5,811.0
SEMrush (broad match) 18,265 36,523 5,811.0
SEMrush (phrase match) 18,265 25,475 5,811.0
Moz (mix of sources) 1,000 1,000 5,811.0
Ubersuggest (related) 1,247 4,261 5,811.0
sale car Google Keyword Planner 2,373 691 5,811.0
Ahrefs (all keyword ideas) 293,908 43,488 5,811.0
Ahrefs (phrase match) 5,811 23,021 5,811.0
SEMrush (broad match) 453,534 36,523 5,811.0
SEMrush (phrase match) 113,664 25,475 5,811.0
Ubersuggest (related) 526 4,261 5,811.0
automotive technician Google Keyword Planner 200 691 5,811.0
Ahrefs (all keyword ideas) 8,834 43,488 5,811.0
Ahrefs (phrase match) 4,396 23,021 5,811.0
SEMrush (broad match) 3,950 36,523 5,811.0
SEMrush (phrase match) 3,607 25,475 5,811.0
Ubersuggest (related) 669 4,261 5,811.0
dealer Google Keyword Planner 456 691 5,811.0
Ahrefs (all keyword ideas) 1,052,918 43,488 5,811.0
Ahrefs (phrase match) 1,037,053 23,021 5,811.0
SEMrush (broad match) 1,246,425 36,523 5,811.0
SEMrush (phrase match) 642,171 25,475 5,811.0
Moz (mix of sources) 1,000 1,000 5,811.0
Ubersuggest (related) 152,426 4,261 5,811.0
Retail what is retail Google Keyword Planner 184 691 2,985.0
Ahrefs (all keyword ideas) 17,705 43,488 2,985.0
Ahrefs (phrase match) 2,370 23,021 2,985.0
SEMrush (broad match) 8,567 36,523 2,985.0
SEMrush (phrase match) 7,505 25,475 2,985.0
Moz (mix of sources) 1,000 1,000 2,985.0
Ubersuggest (related) 315 4,261 2,985.0
retail marketing Google Keyword Planner 293 691 2,985.0
Ahrefs (all keyword ideas) 7,453 43,488 2,985.0
Ahrefs (phrase match) 2,985 23,021 2,985.0
SEMrush (broad match) 11,410 36,523 2,985.0
SEMrush (phrase match) 4,126 25,475 2,985.0
Moz (mix of sources) 1,000 1,000 2,985.0
Ubersuggest (related) 817 4,261 2,985.0
promo code Google Keyword Planner 2,139 691 2,985.0
Ahrefs (all keyword ideas) 908,287 43,488 2,985.0
Ahrefs (phrase match) 855,277 23,021 2,985.0
SEMrush (broad match) 860,316 36,523 2,985.0
SEMrush (phrase match) 758,291 25,475 2,985.0
Moz (mix of sources) 1,000 1,000 2,985.0
Ubersuggest (related) 184,168 4,261 2,985.0
voucher codes Google Keyword Planner 1,546 691 2,985.0
Ahrefs (all keyword ideas) 11,707 43,488 2,985.0
Ahrefs (phrase match) 8,562 23,021 2,985.0
SEMrush (broad match) 62,541 36,523 2,985.0
SEMrush (phrase match) 15,422 25,475 2,985.0
Moz (mix of sources) 1,000 1,000 2,985.0
Ubersuggest (related) 4,487 4,261 2,985.0
retail trade Google Keyword Planner 22 691 2,985.0
Ahrefs (all keyword ideas) 5,067 43,488 2,985.0
Ahrefs (phrase match) 1,352 23,021 2,985.0
SEMrush (broad match) 2,652 36,523 2,985.0
SEMrush (phrase match) 1,662 25,475 2,985.0
Moz (mix of sources) 1,000 1,000 2,985.0
Ubersuggest (related) 218 4,261 2,985.0
Solar Energy solar panels Google Keyword Planner 916 691 4,710.0
Ahrefs (all keyword ideas) 168,715 43,488 4,710.0
Ahrefs (phrase match) 155,362 23,021 4,710.0
SEMrush (broad match) 185,332 36,523 4,710.0
SEMrush (phrase match) 86,195 25,475 4,710.0
Moz (mix of sources) 1,000 1,000 4,710.0
Ubersuggest (related) 21,147 4,261 4,710.0
solar battery charger Google Keyword Planner 797 691 4,710.0
Ahrefs (all keyword ideas) 12,707 43,488 4,710.0
Ahrefs (phrase match) 3,223 23,021 4,710.0
SEMrush (broad match) 5,377 36,523 4,710.0
SEMrush (phrase match) 5,017 25,475 4,710.0
Moz (mix of sources) 1,000 1,000 4,710.0
Ubersuggest (related) 634 4,261 4,710.0
solar panel price Google Keyword Planner 1,096 691 4,710.0
Ahrefs (all keyword ideas) 11,032 43,488 4,710.0
Ahrefs (phrase match) 1,753 23,021 4,710.0
SEMrush (broad match) 4,710 36,523 4,710.0
SEMrush (phrase match) 3,171 25,475 4,710.0
Moz (mix of sources) 1,000 1,000 4,710.0
Ubersuggest (related) 1,058 4,261 4,710.0
free energy Google Keyword Planner 578 691 4,710.0
Ahrefs (all keyword ideas) 59,682 43,488 4,710.0
Ahrefs (phrase match) 46,123 23,021 4,710.0
SEMrush (broad match) 29,545 36,523 4,710.0
SEMrush (phrase match) 28,911 25,475 4,710.0
Moz (mix of sources) 1,000 1,000 4,710.0
Ubersuggest (related) 2,987 4,261 4,710.0
sunpower Google Keyword Planner 388 691 4,710.0
Ahrefs (all keyword ideas) 16,317 43,488 4,710.0
Ahrefs (phrase match) 16,317 23,021 4,710.0
SEMrush (broad match) 9,473 36,523 4,710.0
SEMrush (phrase match) 9,212 25,475 4,710.0
Moz (mix of sources) 1,000 1,000 4,710.0
Ubersuggest (related) 1,631 4,261 4,710.0

To get a sense of the data, we are looking at the distribution of suggestions per SEO tool and keyword category.

2.2 The Raw Data

In the following, we are looking at each search, mapped to SEO tools and keyword categories. The grey, large dot represents the median of each group, here SEO tool which are ranked by this value:

Per Search Eninge:

Key takeaways:

  • This plot highlights how skewed the data is - it might be useful to show the large differences between some SEO tools in combination with specific keyword categories(with a focus on SEO tools).

  • Even though the dots were jittered, overplotting makes it hard to distinguish each data point in the low-value-area - a log scale helps here.

  • Clear ranking when sorted by overall median: Indeed, Ahrefs and SEMrush suggest the highest values, followed by Ubersuggest, Moz, and Google Keyword Planner.

2.3 The Raw Data On a Logarithmic Scale

To see more of the pattern, we transform the x axis to a logarithmic scale (as before with the histograms); here, we use so-called beeswarm plots to make the distribution more clear to the reader:

2.4 Summary Plots of Distributions

Distributions can be effectively summarized as box and whiskers plots which indicate the median (thick line), the interquartile range (box), the minimum and maximum excluding outliers (whiskers), and outliers (points):

Per Keyword Category:

Key takeaways:

  • Ahrefs seems to be more competitive in the categories Home Improvement, Legal, Office Supplies, Online Marketing, Solar Energy, Vacations Travel, and Web Hosting when compared to SEMrush and the other SEO tools.

  • SEMrush on the other hand seems to suggest slightly more when keywords were related to Dating and Wedding and about the same amount as Ahrefs for Gardening and Women Fashion.

The pattern might be easier to read as linerange plot by showing the minimum-maximum range (line) and the median (dot):

2.5 Keywords by Length

Per SEO Tool:

Key takeaways:



Per Keyword Category:

Key takeaways:



2.6 Keywords per Engine & Category

In the following, we investigate the data in a more detailled manner: each keyword, encoded by SEO tool and keyword category.

General Notes

  • Multiples could be ordered by ranking of SEO tools (currently as in csv) and/or keyword categories (curently alphabetic) as well.

  • Median values and/or ranking could be replaced by mean (note: mean may differ between linear and logarithmic scale)

3 Keyword Search Volume

3.1 Data Overview

SEO Tool Minimum Average Median Maximum NAs
Google Keyword Planner 110 15,555.0 3,600 1,500,000 0
Ahrefs 0 9,608.8 2,200 1,780,000 0
SEMrush 20 15,549.2 3,600 1,830,000 0
KeywordTool.io 90 23,483.4 4,400 6,120,000 0
KWFinder 0 14,927.1 3,300 1,390,000 0
LongtailPro 90 15,888.6 3,600 1,500,000 0
SECockpit 70 15,694.7 3,600 1,500,000 0
Sixtrix 25 26,385.1 10,000 250,000 0

Comparison Number of Keywords

3.3 Top Keywords per SEO Tool

4 Keyword Difficulty

4.1 Data Overview

SEO Tool Minimum Average Median Maximum NAs
Ahrefs 0.00 22.54 16.00 99.00 0
SEMrush 0.00 81.17 84.12 99.81 0
KeywordTool.io 0.00 62.62 77.00 100.00 0
KWFinder 6.00 39.14 38.00 86.00 0
LongtailPro 11.00 41.15 41.00 79.00 0
SECockpit 1.29 9.39 7.76 100.00 0
Sixtrix 10.00 50.95 50.00 85.00 0

4.3 Top Keywords per SEO Tool

5 Cost per Click (CPC)

5.1 Data Overview

SEO Tool Minimum Average Median Maximum NAs
Google Keyword Planner 0.02 5.03 2.12 749.06 0
Ahrefs 0.01 3.63 1.60 355.00 0
SEMrush 0.01 3.12 1.38 209.10 0
KeywordTool.io 0.02 4.00 1.60 361.05 0
KWFinder 0.02 4.46 1.66 361.05 0
LongtailPro 0.01 4.33 1.61 361.05 0
SECockpit 0.01 5.25 2.20 502.10 0
Sixtrix 0.11 5.07 1.66 195.47 0

6. Performance in Volume, Difficulty & CPC

6.1 Heatmap of Average Performance

6.2 Engine Performance Compared to Google Keyword Planner

Lollipop Plots of Average Difference

## comparison of average vol, diff and cpc
df_tools_ref <-
  df_tools_long %>% 
  filter(tool == "Google Keyword Planner") %>% 
  group_by(category) %>% 
  summarize(ref = mean(value, na.rm = T)) %>% 
  left_join(filter(df_tools_long, tool != "Google Keyword Planner")) %>% 
  group_by(tool, category) %>% 
  summarize(difference = mean(value, na.rm = T) - unique(ref)) %>% 
ungroup()

theme_lolli <- 
  theme(axis.title.x = element_text(size = 13, color ="grey20"),
        axis.text.x = element_text(size = 11),
        axis.text.y = element_blank(),
        axis.line.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.grid.major.x = element_line(size = 0.6, color = "grey70"),
        plot.margin = margin(5, 20, 5, 20),
        plot.background = element_rect(fill = NA, color = NA))

## plot search volume
lolli_vol <-
  df_tools_ref %>% 
  filter(category == "volume") %>% 
  mutate(
    tool_fct = fct_reorder(tool, difference),
    pos_r = if_else(difference >= 0, difference, NA_real_),
    pos_l = if_else(difference < 0, difference, NA_real_)
  ) %>% 
  ggplot(aes(tool_fct, difference, 
             color = tool)) + 
    geom_segment(aes(x = tool_fct, xend = tool_fct, 
                     y = 0, yend = difference),
                 size = 1.5) +
    geom_hline(yintercept = 0, color = "grey70", size = .9) +
    geom_point(size = 5) +
    geom_text(aes(tool_fct, pos_r, label = tool_fct), 
              hjust = 0, nudge_y = 600,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    geom_text(aes(tool_fct, pos_l, label = tool_fct), 
              hjust = 1,  nudge_y = -600,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    coord_flip() +
    scale_y_continuous(labels = num_format,
                       limits = c(-7000, NA),
                       expand = c(.3, .3)) +
    scale_color_carto_d(palette = "Bold", 
                        limits = levels(df_tools$tool),
                        guide = F) +
    scale_fill_carto_d(palette = "Bold", 
                       limits = levels(df_tools$tool),
                       guide = F) +
    labs(x = NULL, y = "Difference in Average Search Volume") +
    theme_flip + 
    theme_lolli

## plot difficulty scores
lolli_dif <-
  df_tools_ref %>% 
  filter(category == "diff") %>% 
  mutate(
    tool_fct = fct_reorder(tool, difference),
    pos_r = if_else(difference >= 0, difference, NA_real_),
    pos_l = if_else(difference < 0, difference, NA_real_)
  ) %>% 
  ggplot(aes(tool_fct, difference, 
             color = tool)) + 
    geom_segment(aes(x = tool_fct, xend = tool_fct, 
                     y = 0, yend = difference),
                 size = 1.5) +
    geom_hline(yintercept = 0, color = "grey70", size = .9) +
    geom_point(size = 5) +
    geom_text(aes(tool_fct, pos_r, label = tool_fct), 
              hjust = 0, nudge_y = 3,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    geom_text(aes(tool_fct, pos_l, label = tool_fct), 
              hjust = 1,  nudge_y = -3,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    coord_flip() +
    scale_y_continuous(labels = num_format,
                       expand = c(.2, .2)) +
    scale_color_carto_d(palette = "Bold", 
                        limits = levels(df_tools$tool),
                        guide = F) +
    scale_fill_carto_d(palette = "Bold", 
                       limits = levels(df_tools$tool),
                       guide = F) +
    labs(x = NULL, y = "Difference in Average Difficulty Score",
         title = "**Performance of SEO Tools in Comparison to Google Keyword Planner** (comparison of averages)") +
    theme_flip + 
    theme_lolli +
    theme(plot.title = element_markdown(size = 22, margin = margin(b = 20)))

## plot cost per click
lolli_cpc <-
  df_tools_ref %>% 
  filter(category == "cpc") %>% 
  mutate(
    tool_fct = fct_reorder(tool, difference),
    pos_r = if_else(difference >= 0, difference, NA_real_),
    pos_l = if_else(difference < 0, difference, NA_real_)
  ) %>% 
  ggplot(aes(tool_fct, difference, 
             color = tool)) + 
    geom_segment(aes(x = tool_fct, xend = tool_fct, 
                     y = 0, yend = difference),
                 size = 1.5) +
    geom_hline(yintercept = 0, color = "grey70", size = .9) +
    geom_point(size = 5) +
    geom_text(aes(tool_fct, pos_r, label = tool_fct), 
              hjust = 0, nudge_y = .1,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    geom_text(aes(tool_fct, pos_l, label = tool_fct), 
              hjust = 1,  nudge_y = -.1,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    coord_flip() +
    scale_y_continuous(labels = num_format,
                       limits = c(-1.7, .5),
                       expand = c(.2, .2)) +
    scale_color_carto_d(palette = "Bold", 
                        limits = levels(df_tools$tool),
                        guide = F) +
    scale_fill_carto_d(palette = "Bold", 
                       limits = levels(df_tools$tool),
                       guide = F) +
    labs(x = NULL, y = "Difference in Average Cost per Click (CPC)") +
    theme_flip + 
    theme_lolli

lolli_vol + lolli_dif + lolli_cpc + plot_layout(nrow = 1)

Lollipop Plots of Median Difference

## comparison of median vol, diff and cpc
df_tools_ref_m <-
  df_tools_long %>% 
  filter(tool == "Google Keyword Planner") %>% 
  group_by(category) %>% 
  summarize(ref = median(value, na.rm = T)) %>% 
  left_join(filter(df_tools_long, tool != "Google Keyword Planner")) %>% 
  group_by(tool, category) %>% 
  summarize(difference = median(value, na.rm = T) - unique(ref)) %>% 
  ungroup()

## plot search volume
lolli_vol <-
  df_tools_ref_m %>% 
  filter(category == "volume") %>% 
  mutate(
    tool_fct = fct_reorder(tool, difference),
    pos_r = if_else(difference >= 0, difference, NA_real_),
    pos_l = if_else(difference < 0, difference, NA_real_)
  ) %>% 
  ggplot(aes(tool_fct, difference, 
             color = tool)) + 
    geom_segment(aes(x = tool_fct, xend = tool_fct, 
                     y = 0, yend = difference),
                 size = 1.5) +
    geom_hline(yintercept = 0, color = "grey70", size = .9) +
    geom_point(size = 5) +
    geom_text(aes(tool_fct, pos_r, label = tool_fct), 
              hjust = 0, nudge_y = 400,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    geom_text(aes(tool_fct, pos_l, label = tool_fct), 
              hjust = 1,  nudge_y = -400,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    coord_flip() +
    scale_y_continuous(labels = num_format,
                       limits = c(-3000, NA),
                       expand = c(.15, .15)) +
    scale_color_carto_d(palette = "Bold", 
                        limits = levels(df_tools$tool),
                        guide = F) +
    scale_fill_carto_d(palette = "Bold", 
                       limits = levels(df_tools$tool),
                       guide = F) +
    labs(x = NULL, y = "Difference in Median Search Volume") +
    theme_flip + 
    theme_lolli

## plot difficulty scores
lolli_dif <-
  df_tools_ref_m %>% 
  filter(category == "diff") %>% 
  mutate(
    tool_fct = fct_reorder(tool, difference),
    pos_r = if_else(difference >= 0, difference, NA_real_),
    pos_l = if_else(difference < 0, difference, NA_real_)
  ) %>% 
  ggplot(aes(tool_fct, difference, 
             color = tool)) + 
    geom_segment(aes(x = tool_fct, xend = tool_fct, 
                     y = 0, yend = difference),
                 size = 1.5) +
    geom_hline(yintercept = 0, color = "grey70", size = .9) +
    geom_point(size = 5) +
    geom_text(aes(tool_fct, pos_r, label = tool_fct), 
              hjust = 0, nudge_y = 3,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    geom_text(aes(tool_fct, pos_l, label = tool_fct), 
              hjust = 1,  nudge_y = -3,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    coord_flip() +
    scale_y_continuous(labels = num_format,
                       limits = c(-80, 45),
                       expand = c(.1, .1)) +
    scale_color_carto_d(palette = "Bold", 
                        limits = levels(df_tools$tool),
                        guide = F) +
    scale_fill_carto_d(palette = "Bold", 
                       limits = levels(df_tools$tool),
                       guide = F) +
    labs(x = NULL, y = "Difference in Median Difficulty Score",
         title = "**Performance of SEO Tools in Comparison to Google Keyword Planner** (comparison of medians)") +
    theme_flip + 
    theme_lolli +
    theme(plot.title = element_markdown(size = 22, margin = margin(b = 20)))

## plot cost per click
lolli_cpc <-
  df_tools_ref_m %>% 
  filter(category == "cpc") %>% 
  mutate(
    tool_fct = fct_reorder(tool, difference),
    pos_r = if_else(difference >= 0, difference, NA_real_),
    pos_l = if_else(difference < 0, difference, NA_real_)
  ) %>% 
  ggplot(aes(tool_fct, difference, 
             color = tool)) + 
    geom_segment(aes(x = tool_fct, xend = tool_fct, 
                     y = 0, yend = difference),
                 size = 1.5) +
    geom_hline(yintercept = 0, color = "grey70", size = .9) +
    geom_point(size = 5) +
    geom_text(aes(tool_fct, pos_r, label = tool_fct), 
              hjust = 0, nudge_y = .08,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    geom_text(aes(tool_fct, pos_l, label = tool_fct), 
              hjust = 1,  nudge_y = -.08,
              family = "Montserrat", 
              fontface = "bold", size = 4.7) +
    coord_flip() +
    scale_y_continuous(labels = num_format,
                       limits = c(-.9, .3),
                       expand = c(.2, .2)) +
    scale_color_carto_d(palette = "Bold", 
                        limits = levels(df_tools$tool),
                        guide = F) +
    scale_fill_carto_d(palette = "Bold", 
                       limits = levels(df_tools$tool),
                       guide = F) +
    labs(x = NULL, y = "Difference in Median Cost per Click (CPC)") +
    theme_flip + 
    theme_lolli

lolli_vol + lolli_dif + lolli_cpc + plot_layout(nrow = 1)

6.3 Heatmap

TO DO:

  • Lollipops + Boxplots per Volume Category
  • How to deal with min ranked keywords? Try again for Difficulty + CPC! (also makes more sense than volume)
  • Heatmap with largest difference

Session Info

## R version 3.6.2 (2019-12-12)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17763)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=German_Germany.1252  LC_CTYPE=German_Germany.1252   
## [3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C                   
## [5] LC_TIME=German_Germany.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] patchwork_1.0.0.9000 ggsci_2.9            rcartocolor_2.0.0   
##  [4] kableExtra_1.1.0     ggtext_0.1.0         ggbeeswarm_0.6.0    
##  [7] showtext_0.7         showtextdb_2.0       sysfonts_0.8        
## [10] janitor_1.2.0        rmdformats_0.3.6     inspectdf_0.0.7     
## [13] DataExplorer_0.8.1   knitr_1.26           fs_1.3.1            
## [16] imager_0.41.2        magrittr_1.5         magick_2.2          
## [19] glue_1.3.1           treemapify_2.5.3     reshape2_1.4.3      
## [22] prettydoc_0.3.1      mblm_0.12.1          sm_2.2-5.6          
## [25] DT_0.10              sentimentr_2.7.1     tidytext_0.2.2      
## [28] here_0.1             gganimate_1.0.4      scales_1.1.0        
## [31] plotly_4.9.1         extrafont_0.17       hrbrthemes_0.7.1    
## [34] ggthemes_4.2.0       readxl_1.3.1         forcats_0.4.0       
## [37] stringr_1.4.0        dplyr_0.8.3          purrr_0.3.3         
## [40] readr_1.3.1          tidyr_1.0.0          tibble_2.1.3        
## [43] ggplot2_3.2.1        tidyverse_1.3.0     
## 
## loaded via a namespace (and not attached):
##  [1] backports_1.1.5   systemfonts_0.1.1 plyr_1.8.5        igraph_1.2.4.2   
##  [5] selectr_0.4-2     lazyeval_0.2.2    SnowballC_0.6.0   digest_0.6.23    
##  [9] htmltools_0.4.0   tiff_0.1-5        fansi_0.4.0       ggfittext_0.8.1  
## [13] modelr_0.1.5      extrafontdb_1.0   prettyunits_1.0.2 jpeg_0.1-8.1     
## [17] colorspace_1.4-1  rvest_0.3.5       haven_2.2.0       xfun_0.11        
## [21] tcltk_3.6.2       crayon_1.3.4      jsonlite_1.6      zeallot_0.1.0    
## [25] gtable_0.3.0      webshot_0.5.2     scico_1.1.0       Rttf2pt1_1.3.7   
## [29] DBI_1.0.0         qdapRegex_0.7.2   Rcpp_1.0.3        viridisLite_0.3.0
## [33] progress_1.2.2    gridtext_0.1.0    textclean_0.9.3   htmlwidgets_1.5.1
## [37] httr_1.4.1        ellipsis_0.3.0    pkgconfig_2.0.3   farver_2.0.1     
## [41] dbplyr_1.4.2      tidyselect_0.2.5  labeling_0.3      rlang_0.4.2      
## [45] munsell_0.5.0     cellranger_1.1.0  tools_3.6.2       cli_2.0.0        
## [49] generics_0.0.2    gifski_0.8.6      broom_0.5.2       evaluate_0.14    
## [53] yaml_2.2.0        readbitmap_0.1.5  nlme_3.1-142      xml2_1.2.2       
## [57] tokenizers_0.2.1  compiler_3.6.2    rstudioapi_0.10   beeswarm_0.2.3   
## [61] curl_4.3          png_0.1-7         reprex_0.3.0      syuzhet_1.0.4    
## [65] tweenr_1.0.1      stringi_1.4.3     highr_0.8         gdtools_0.2.1    
## [69] lattice_0.20-38   Matrix_1.2-18     markdown_1.1      vctrs_0.2.0      
## [73] pillar_1.4.2      lifecycle_0.1.0   networkD3_0.4     data.table_1.12.8
## [77] R6_2.4.1          bookdown_0.16     gridExtra_2.3     bmp_0.3          
## [81] vipor_0.4.5       janeaustenr_0.1.5 lexicon_1.2.1     assertthat_0.2.1 
## [85] rprojroot_1.3-2   withr_2.1.2       parallel_3.6.2    hms_0.5.2        
## [89] grid_3.6.2        rmarkdown_2.0     snakecase_0.11.0  lubridate_1.7.4